Certain graphical applications are used for segmenting and labeling 3D models. Segmenting a 3D model involves identifying component shapes that, when combined, form the 3D model. In one example, segmenting a 3D model of a car involves decomposing the 3D model into component shapes such as a body, wheels, a windshield, a roof, a trunk, etc. Labelling the 3D model involves applying a label or other identifier to component shapes (e.g., labeling a first component shape as a “body,” a second component shape as a “hood,” etc.).
Some existing solutions for segmenting 3D models involve training segmentation algorithms in a highly supervised manner. For instance, to accurately train these segmentation algorithms, a user manually generates pre-labeled training data for training segmentation algorithms before newly encountered 3D models are segmented using the segmentation algorithms. The user is required to identify different examples of component shapes for a 3D model. Furthermore, the user must consistently apply the same labeling to similar types of component shapes in different 3D models. Thus, generating this pre-labeled training data involves extensive manual effort.
Certain publicly available data on different 3D models includes labeling information for the 3D models. For example, a public website may allow different artists (e.g., hobbyists, videogame designers, etc.) to independently contribute various 3D models. Each artist applies a subjective labelling scheme that is specific to the artists' needs, preferences, or workflows. These subjective labels are not applied for the purpose of enabling segmentation of newly encountered shapes. Thus, this publicly available 3D model data lacks a consistent labeling scheme required for training certain existing segmentation algorithms.
Other existing solutions involve segmentation algorithms that automatically assign component shapes of a 3D model without requiring pre-labeled training data. But, because these segmentation algorithms do not assign user-provided labels to component shapes, the resulting labels lack any semantic meaning. For instance, applying these segmentation algorithms to a 3D model of a car would result in certain component shapes being assigned labels such as “cluster 1” and “cluster 2,” which do not convey that a particular component shape is a hood, a trunk, a wheel, a window, etc.